In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all usef...In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.展开更多
The use of hidden conditional random fields （HCRFs） for tone modeling is explored. The tone recognition performance is improved using HCRFs by taking advantage of intra-syllable dynamic, inter-syllable dynamic and d...The use of hidden conditional random fields （HCRFs） for tone modeling is explored. The tone recognition performance is improved using HCRFs by taking advantage of intra-syllable dynamic, inter-syllable dynamic and duration features. When the tone model is integrated into continuous speech recognition, the discriminative model weight training （DMWT） is proposed. Acoustic and tone scores are scaled by model weights discriminatively trained by the minimum phone error （MPE） criterion. Two schemes of weight training are evaluated and a smoothing technique is used to make training robust to overtraining problem. Experiments show that the accuracies of tone recognition and large vocabulary continuous speech recognition （LVCSR） can be improved by the HCRFs based tone model. Compared with the global weight scheme, continuous speech recognition can be improved by the discriminative trained weight combinations.展开更多
A class of nonlinear and continuous type Leontief model and its corresponding conditional input-output equation are introduced, and two basic problems under the so called positive or negative boundary assumption are p...A class of nonlinear and continuous type Leontief model and its corresponding conditional input-output equation are introduced, and two basic problems under the so called positive or negative boundary assumption are presented. By approaches of nonlinear analysis some solvability results of this equation and continuous perturbation properties of the relative solution sets are obtained, and some economic significance are illustrated by the remark.展开更多
With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service respons...With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.展开更多
In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is es...In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.展开更多
Using a simplified nonlinearly theoretical grassland ecosystem proposed by Zeng et al.,we study the sensitivity and nonlinear instability of the grassland ecosystem to finiteamplitude initial perturbations with the ap...Using a simplified nonlinearly theoretical grassland ecosystem proposed by Zeng et al.,we study the sensitivity and nonlinear instability of the grassland ecosystem to finiteamplitude initial perturbations with the approach of conditional nonlinear optimal perturbation （CNOP）.The results show that the linearly stable grassland （desert or latent desert） states can turn to be nonlinearly unstable with finite amplitude initial perturbations.When the precipitation is between the two bifurcation points,a large enough finite amplitude initial perturbation can induce a transition between the grassland statethe desert state or the latent desert.展开更多
This paper aims to explore how learners of English as a second language(ESL)acquire English conditional sentences and what causes their difficulties,especially focusing on how their native languages affect their acqui...This paper aims to explore how learners of English as a second language(ESL)acquire English conditional sentences and what causes their difficulties,especially focusing on how their native languages affect their acquisition of the conditional sentences.Interviews were carried out with four undergraduate ESL students of University of Central Oklahoma in the United States who are respectively Chinese,Korean,French,and Greek.By conducting interviews with them,the participants’perceptions of acquiring English conditional sentences will be collected and analyzed.There will be some typical errors of constructing conditional sentences demonstrated.Moreover,some pedagogical implications will also be provided,which will help students have a better command of the conditional sentences.展开更多
Let （M,τ） be a noncommutative probability space, （Mn）n≥l a sequence of von Neumann subalgebras of M and N a von Neumann subalgebra of M. We introduce the notions of It-approach and orthogonal approach for （Mn）...Let （M,τ） be a noncommutative probability space, （Mn）n≥l a sequence of von Neumann subalgebras of M and N a von Neumann subalgebra of M. We introduce the notions of It-approach and orthogonal approach for （Mn）n≥1 and prove that ε（x｜Mn）Lp→ε（x｜N） for any x ∈ Lp（M） （1 ≤ p 〈 ∞） if and only if （Mn）n≥1 τ-approaches and orthogonally approaches N.展开更多
Using a direct perturbation method, we investigate the stability of a diatomic molecule modelled by a weakly laser-driven Morse oscillator. It is shown that stationary state solution of the system is stable in the sen...Using a direct perturbation method, we investigate the stability of a diatomic molecule modelled by a weakly laser-driven Morse oscillator. It is shown that stationary state solution of the system is stable in the sense of Lyapunov and the periodical one possesses conditional stability, namely its stability depends on the initial conditions and system parameters. The corresponding sufficient and necessary conditions are established that indicate the stable states associated with some discrete energies. The results reveal how a diatomic molecule can be stabilized or dissociated with a weak laser, and demonstrate that the mathematical conditional stability works in the considered physical system.展开更多
A fast method for phrase structure grammar analysis is proposed based on conditional ran- dom fields （CRF）. The method trains several CRF classifiers for recognizing the phrase nodes at dif- ferent levels, and uses ...A fast method for phrase structure grammar analysis is proposed based on conditional ran- dom fields （CRF）. The method trains several CRF classifiers for recognizing the phrase nodes at dif- ferent levels, and uses the bottom-up to connect the recognized phrase nodes to construct the syn- tactic tree. On the basis of Beijing forest studio Chinese tagged corpus, two experiments are de- signed to select the training parameters and verify the validity of the method. The result shows that the method costs 78. 98 ms and 4. 63 ms to train and test a Chinese sentence of 17. 9 words. The method is a new way to parse the phrase structure grammar for Chinese, and has good generalization ability and fast speed.展开更多
Implementation of a nonlocal multi-qubit conditional phase gate is an essential requirement in some quantum infor- mation processing （QIP） tasks. Recently, a novel solid-state cavity quantum electrodynamics （QED） ...Implementation of a nonlocal multi-qubit conditional phase gate is an essential requirement in some quantum infor- mation processing （QIP） tasks. Recently, a novel solid-state cavity quantum electrodynamics （QED） system, in which the nitrogen-vacancy （NV） center in diamond is coupled to a microtoroidal resonator （MTR）, has been proposed as a poten- tial system for hybrid quantum information and computing. By virtue of such systems, we present a scheme to realize a nonlocal N-qubit conditional phase gate directly. Our scheme employs a cavity input-output process and single-photon interference, without the use of any auxiliary entanglement pair or classical communication. Considering the currently available technologies, our scheme might be quite useful among different nodes in quantum networks for large-scaled QIP.展开更多
Conditionals are often divided into two categories: real conditional and unreal conditional. This paper will only discuss the former. As for the latter, it has been explained comparative exhaustively both in tradition...Conditionals are often divided into two categories: real conditional and unreal conditional. This paper will only discuss the former. As for the latter, it has been explained comparative exhaustively both in traditional grammar and contemporary grammar. In fact, real conditional is much more difficult than it is thought, because people have not described it as exhaustively as unreal conditional.展开更多
Identifying gene names is an attractive research area of biology computing. However, accurate extraction of gene names is a challenging task with the lack of conventions for describing gene names. We devise a systemat...Identifying gene names is an attractive research area of biology computing. However, accurate extraction of gene names is a challenging task with the lack of conventions for describing gene names. We devise a systematical architecture and apply the model using conditional random fields （CRFs） for extracting gene names from Medline. In order to improve the performance, biomedical ontology features are inserted into the model and post processing including boundary adjusting and word filter is presented to solve name overlapping problem and remove false positive single words. Pure string match method, baseline CRFs, and CRFs with our methods are applied to human gene names and HIV gene names extraction respectively in 1100 abstracts of Medline and their performances are contrasted. Results show that CRFs are robust for unseen gene names. Furthermore, CRFs with our methods outperforms other methods with precision 0.818 and recall 0.812.展开更多
In order to eliminate Courant-Friedrich-Levy(CFL) condition restraint and improvecomputational efficiency,a new finite-difference time-domain(FDTD)method based on the alternating-direction implicit(ADI) technique is i...In order to eliminate Courant-Friedrich-Levy(CFL) condition restraint and improvecomputational efficiency,a new finite-difference time-domain(FDTD)method based on the alternating-direction implicit(ADI) technique is introduced recently.In this paper,a theoretical proof of the stabilityof the three-dimensional(3-D)ADI-FDTD method is presented.It is shown that the 3-D ADI-FDTDmethod is unconditionally stable and free from the CFL condition restraint.展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
Under complex flight conditions,such as obstacle avoidance and extreme sea state,wing-in-ground(WIG)effect aircraft need to ascend to higher altitudes,resulting in the disappearance of the ground effect.A design of hi...Under complex flight conditions,such as obstacle avoidance and extreme sea state,wing-in-ground(WIG)effect aircraft need to ascend to higher altitudes,resulting in the disappearance of the ground effect.A design of high-speed WIG airfoil considering non-ground effect is carried out by a novel two-step inverse airfoil design method that combines conditional generative adversarial network(CGAN)and artificial neural network(ANN).The CGAN model is employed to generate a variety of airfoil designs that satisfy the desired lift-drag ratios in both ground effect and non-ground effect conditions.Subsequently,the ANN model is utilized to forecast aerodynamic parameters of the generated airfoils.The results indicate that the CGAN model contributes to a high accuracy rate for airfoil design and enables the creation of novel airfoil designs.Furthermore,it demonstrates high accuracy in predicting aerodynamic parameters of these airfoils due to the ANN model.This method eliminates the necessity for numerical simulations and experimental testing through the design procedure,showcasing notable efficiency.The analysis of airfoils generated by the CGAN model shows that airfoils exhibiting high lift-drag ratios under both flight conditions typically have cambers of among[0.08c,0.105c],with the positions of maximum camber occurring among[0.35c,0.5c]of the chord length,and the leading-edge radiuses of these airfoils primarily cluster among[0.008c,0.025c]展开更多
The sap flow method is widely used to estimate forest transpiration.However,at the individual tree level it has spatiotemporal variations due to the impacts of environmental conditions and spatial relationships among ...The sap flow method is widely used to estimate forest transpiration.However,at the individual tree level it has spatiotemporal variations due to the impacts of environmental conditions and spatial relationships among trees.Therefore,an in-depth understanding of the coupling effects of these factors is important for designing sap flow measurement methods and performing accurate assessments of stand scale transpiration.This study is based on observations of sap flux density(SF_(d))of nine sample trees with different Hegyi’s competition indices(HCIs),soil moisture,and meteorological conditions in a pure plantation of Larix gmelinii var.principis-rupprechtii during the 2021 growing season(May to September).A multifactorial model of sap flow was developed and possible errors in the stand scale sap flow estimates associated with sample sizes were determined using model-based predictions of sap flow.Temporal variations are controlled by vapour pressure deficit(VPD),solar radiation(R),and soil moisture,and these relationships can be described by polynomial or saturated exponential functions.Spatial(individual)differences were influenced by the HCI,as shown by the decaying power function.A simple SF_(d)model at the individual tree level was developed to describe the synergistic influences of VPD,R,soil moisture,and HCI.The coefficient of variations(CV)of the sap flow estimates gradually stabilized when the sample size was>10;at least six sample trees were needed if the CV was within 10%.This study improves understanding of the mechanisms of spatiotemporal variations in sap flow at the individual tree level and provides a new methodology for determining the optimal sample size for sap flow measurements.展开更多
BACKGROUND: This study aimed to explore the risk factors associated with intensive care unitacquired weakness(ICU-AW) in critically ill patients at risk of malnutrition and to evaluate the efficacy of early enteral nu...BACKGROUND: This study aimed to explore the risk factors associated with intensive care unitacquired weakness(ICU-AW) in critically ill patients at risk of malnutrition and to evaluate the efficacy of early enteral nutrition(EEN) and the role of biomarkers in managing ICU-AW.METHODS: This retrospective, observational cohort study included 180 patients at risk of malnutrition admitted to the emergency intensive care unit of the First Affiliated Hospital of Xiamen University Hospital from January 2022 to December 2023. Patients were divided into ICU-AW group and non-ICU-AW group according to whether they developed ICU-AW, or categorized into EEN and parenteral nutrition(PN) groups according to nutritional support. ICU-AW was diagnosed using the Medical Research Council score. The primary outcome was the occurrence of ICU-AW.RESULTS: The significant factors associated with ICU-AW included age, sex, type of nutritional therapy, mechanical ventilation(MV), body mass index(BMI), blood urea nitrogen(BUN), and creatinine(Cr) levels(P<0.05). The PN group developed ICU-AW earlier than did the EEN group, with a significant difference observed(log-rank P<0.001). Among biomarkers for ICU-AW, the mean prealbumin(PAB)/C-reactive protein(CRP) ratio had the highest diagnostic accuracy(area under the curve [AUC] 0.928, 95% confidence interval [95% CI] 0.892–0.946), surpassing the mean Cr/BUN ratio(AUC 0.740, 95% CI 0.663–0.819) and mean transferrin levels(AUC 0.653, 95% CI 0.574–0.733).CONCLUSION: Independent risk factors for ICU-AW include female sex, advanced age, PN, MV, lower BMI, and elevated BUN and Cr levels. EEN may potentially delay ICU-AW onset, and the PAB/CRP ratio may be an effective diagnostic marker for this condition.展开更多
Purpose:This study investigates key factors contributing to research impact and their interactions with the Research Impact Quintuple Helix Model by Arsalan et al.(2024).Design/methodology/approach:Using data from a g...Purpose:This study investigates key factors contributing to research impact and their interactions with the Research Impact Quintuple Helix Model by Arsalan et al.(2024).Design/methodology/approach:Using data from a global survey of 630 scientists across diverse disciplines,genders,regions,and experience levels,Structural Equation Modelling(SEM)was employed to assess the influence of 29 factors related to researcher characteristics,research attributes,publication strategies,institutional support,and national roles.Findings:The study validated the Quintuple Helix Model,uncovering complex interdependencies.Institutional support significantly affects research impact by covering leadership,resources,recognition,and funding.Researcher attributes,including academic experience and domain knowledge,also play a crucial role.National socioeconomic conditions indirectly influence research impact by supporting institutions,underscoring the importance of conducive national frameworks.Research limitations:While the study offers valuable insights,it has limitations.Although statistically sufficient,the response rate was below 10%,suggesting that the findings may not fully represent the entire global research community.The reliance on self-reported data may also introduce bias,as perceptions of impact can be subjective.Practical implications:The findings have a significant impact on researchers aiming to enhance their work’s societal,economic,and cultural significance,institutions seeking supportive environments,and policymakers interested in creating favourable national conditions for impactful research.The study advocates for a strategic alignment among national policies,institutional practices,and individual researcher efforts to maximise research impact and effectively address global challenges.Originality/value:By empirically validating the Research Impact Quintuple Helix Model,this study offers a holistic framework for understanding the synergy of factors that drive impactful research.展开更多
基金Outstanding Youth Foundation of Hunan Provincial Department of Education(Grant No.22B0911)。
文摘In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.
文摘The use of hidden conditional random fields （HCRFs） for tone modeling is explored. The tone recognition performance is improved using HCRFs by taking advantage of intra-syllable dynamic, inter-syllable dynamic and duration features. When the tone model is integrated into continuous speech recognition, the discriminative model weight training （DMWT） is proposed. Acoustic and tone scores are scaled by model weights discriminatively trained by the minimum phone error （MPE） criterion. Two schemes of weight training are evaluated and a smoothing technique is used to make training robust to overtraining problem. Experiments show that the accuracies of tone recognition and large vocabulary continuous speech recognition （LVCSR） can be improved by the HCRFs based tone model. Compared with the global weight scheme, continuous speech recognition can be improved by the discriminative trained weight combinations.
文摘A class of nonlinear and continuous type Leontief model and its corresponding conditional input-output equation are introduced, and two basic problems under the so called positive or negative boundary assumption are presented. By approaches of nonlinear analysis some solvability results of this equation and continuous perturbation properties of the relative solution sets are obtained, and some economic significance are illustrated by the remark.
基金supported by Science and Technology Project of State Grid Corporation(Research and Application of Intelligent Energy Meter Quality Analysis and Evaluation Technology Based on Full Chain Data)
文摘With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.
文摘In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.
基金Supported by the NSF of Chian（4080502010702050+1 种基金60704015） Supported by the Natural Science Foundation of Henan Education Department（2010A100003）
文摘Using a simplified nonlinearly theoretical grassland ecosystem proposed by Zeng et al.,we study the sensitivity and nonlinear instability of the grassland ecosystem to finiteamplitude initial perturbations with the approach of conditional nonlinear optimal perturbation （CNOP）.The results show that the linearly stable grassland （desert or latent desert） states can turn to be nonlinearly unstable with finite amplitude initial perturbations.When the precipitation is between the two bifurcation points,a large enough finite amplitude initial perturbation can induce a transition between the grassland statethe desert state or the latent desert.
文摘This paper aims to explore how learners of English as a second language(ESL)acquire English conditional sentences and what causes their difficulties,especially focusing on how their native languages affect their acquisition of the conditional sentences.Interviews were carried out with four undergraduate ESL students of University of Central Oklahoma in the United States who are respectively Chinese,Korean,French,and Greek.By conducting interviews with them,the participants’perceptions of acquiring English conditional sentences will be collected and analyzed.There will be some typical errors of constructing conditional sentences demonstrated.Moreover,some pedagogical implications will also be provided,which will help students have a better command of the conditional sentences.
基金supported by National Natural Science Foundation of China(11271293,11471251)the Research Fund for the Doctoral Program of Higher Education of China(2014201020205)
文摘Let （M,τ） be a noncommutative probability space, （Mn）n≥l a sequence of von Neumann subalgebras of M and N a von Neumann subalgebra of M. We introduce the notions of It-approach and orthogonal approach for （Mn）n≥1 and prove that ε（x｜Mn）Lp→ε（x｜N） for any x ∈ Lp（M） （1 ≤ p 〈 ∞） if and only if （Mn）n≥1 τ-approaches and orthogonally approaches N.
基金Project supported by the National Natural Science Foundation of China （Grant No 10275023）, and by the Science Foundation of the Laboratory of Magnetic Resonance and Atomic and Molecular Physics of China （Grant No T152504）.
文摘Using a direct perturbation method, we investigate the stability of a diatomic molecule modelled by a weakly laser-driven Morse oscillator. It is shown that stationary state solution of the system is stable in the sense of Lyapunov and the periodical one possesses conditional stability, namely its stability depends on the initial conditions and system parameters. The corresponding sufficient and necessary conditions are established that indicate the stable states associated with some discrete energies. The results reveal how a diatomic molecule can be stabilized or dissociated with a weak laser, and demonstrate that the mathematical conditional stability works in the considered physical system.
基金Supported by the Science and Technology Innovation Plan of Beijing Institute of Technology(2013)
文摘A fast method for phrase structure grammar analysis is proposed based on conditional ran- dom fields （CRF）. The method trains several CRF classifiers for recognizing the phrase nodes at dif- ferent levels, and uses the bottom-up to connect the recognized phrase nodes to construct the syn- tactic tree. On the basis of Beijing forest studio Chinese tagged corpus, two experiments are de- signed to select the training parameters and verify the validity of the method. The result shows that the method costs 78. 98 ms and 4. 63 ms to train and test a Chinese sentence of 17. 9 words. The method is a new way to parse the phrase structure grammar for Chinese, and has good generalization ability and fast speed.
基金Project supported by the National Fundamental Research Program of China(Grant No.2010CB923202)the Fundamental Research Funds for the Central Universities,Chinathe National Natural Science Foundation of China(Grant Nos.61177085,61205117,and 61377097)
文摘Implementation of a nonlocal multi-qubit conditional phase gate is an essential requirement in some quantum infor- mation processing （QIP） tasks. Recently, a novel solid-state cavity quantum electrodynamics （QED） system, in which the nitrogen-vacancy （NV） center in diamond is coupled to a microtoroidal resonator （MTR）, has been proposed as a poten- tial system for hybrid quantum information and computing. By virtue of such systems, we present a scheme to realize a nonlocal N-qubit conditional phase gate directly. Our scheme employs a cavity input-output process and single-photon interference, without the use of any auxiliary entanglement pair or classical communication. Considering the currently available technologies, our scheme might be quite useful among different nodes in quantum networks for large-scaled QIP.
文摘Conditionals are often divided into two categories: real conditional and unreal conditional. This paper will only discuss the former. As for the latter, it has been explained comparative exhaustively both in traditional grammar and contemporary grammar. In fact, real conditional is much more difficult than it is thought, because people have not described it as exhaustively as unreal conditional.
基金supported by China Scholarship Council under Grant No 2007104897UESTC Youth Foundation under Grant No JX05007
文摘Identifying gene names is an attractive research area of biology computing. However, accurate extraction of gene names is a challenging task with the lack of conventions for describing gene names. We devise a systematical architecture and apply the model using conditional random fields （CRFs） for extracting gene names from Medline. In order to improve the performance, biomedical ontology features are inserted into the model and post processing including boundary adjusting and word filter is presented to solve name overlapping problem and remove false positive single words. Pure string match method, baseline CRFs, and CRFs with our methods are applied to human gene names and HIV gene names extraction respectively in 1100 abstracts of Medline and their performances are contrasted. Results show that CRFs are robust for unseen gene names. Furthermore, CRFs with our methods outperforms other methods with precision 0.818 and recall 0.812.
基金Supported by the Specialized Research Fund for the Doctoral Program of Higher Education(No.20010614003)
文摘In order to eliminate Courant-Friedrich-Levy(CFL) condition restraint and improvecomputational efficiency,a new finite-difference time-domain(FDTD)method based on the alternating-direction implicit(ADI) technique is introduced recently.In this paper,a theoretical proof of the stabilityof the three-dimensional(3-D)ADI-FDTD method is presented.It is shown that the 3-D ADI-FDTDmethod is unconditionally stable and free from the CFL condition restraint.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
基金supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,the Fundamental Research Funds for the Central Universities(No.ILA220101A23)CARDC Fundamental and Frontier Technology Research Fund(No.PJD20200210)the Aeronautical Science Foundation of China(No.20200023052002).
文摘Under complex flight conditions,such as obstacle avoidance and extreme sea state,wing-in-ground(WIG)effect aircraft need to ascend to higher altitudes,resulting in the disappearance of the ground effect.A design of high-speed WIG airfoil considering non-ground effect is carried out by a novel two-step inverse airfoil design method that combines conditional generative adversarial network(CGAN)and artificial neural network(ANN).The CGAN model is employed to generate a variety of airfoil designs that satisfy the desired lift-drag ratios in both ground effect and non-ground effect conditions.Subsequently,the ANN model is utilized to forecast aerodynamic parameters of the generated airfoils.The results indicate that the CGAN model contributes to a high accuracy rate for airfoil design and enables the creation of novel airfoil designs.Furthermore,it demonstrates high accuracy in predicting aerodynamic parameters of these airfoils due to the ANN model.This method eliminates the necessity for numerical simulations and experimental testing through the design procedure,showcasing notable efficiency.The analysis of airfoils generated by the CGAN model shows that airfoils exhibiting high lift-drag ratios under both flight conditions typically have cambers of among[0.08c,0.105c],with the positions of maximum camber occurring among[0.35c,0.5c]of the chord length,and the leading-edge radiuses of these airfoils primarily cluster among[0.008c,0.025c]
基金supported by the Fundamental Research Funds of the Chinese Academy of Forestry(CAFYBB2020QB004)the National Natural Science Foundation of China(41971038,32171559,U20A2085,and U21A2005).
文摘The sap flow method is widely used to estimate forest transpiration.However,at the individual tree level it has spatiotemporal variations due to the impacts of environmental conditions and spatial relationships among trees.Therefore,an in-depth understanding of the coupling effects of these factors is important for designing sap flow measurement methods and performing accurate assessments of stand scale transpiration.This study is based on observations of sap flux density(SF_(d))of nine sample trees with different Hegyi’s competition indices(HCIs),soil moisture,and meteorological conditions in a pure plantation of Larix gmelinii var.principis-rupprechtii during the 2021 growing season(May to September).A multifactorial model of sap flow was developed and possible errors in the stand scale sap flow estimates associated with sample sizes were determined using model-based predictions of sap flow.Temporal variations are controlled by vapour pressure deficit(VPD),solar radiation(R),and soil moisture,and these relationships can be described by polynomial or saturated exponential functions.Spatial(individual)differences were influenced by the HCI,as shown by the decaying power function.A simple SF_(d)model at the individual tree level was developed to describe the synergistic influences of VPD,R,soil moisture,and HCI.The coefficient of variations(CV)of the sap flow estimates gradually stabilized when the sample size was>10;at least six sample trees were needed if the CV was within 10%.This study improves understanding of the mechanisms of spatiotemporal variations in sap flow at the individual tree level and provides a new methodology for determining the optimal sample size for sap flow measurements.
文摘BACKGROUND: This study aimed to explore the risk factors associated with intensive care unitacquired weakness(ICU-AW) in critically ill patients at risk of malnutrition and to evaluate the efficacy of early enteral nutrition(EEN) and the role of biomarkers in managing ICU-AW.METHODS: This retrospective, observational cohort study included 180 patients at risk of malnutrition admitted to the emergency intensive care unit of the First Affiliated Hospital of Xiamen University Hospital from January 2022 to December 2023. Patients were divided into ICU-AW group and non-ICU-AW group according to whether they developed ICU-AW, or categorized into EEN and parenteral nutrition(PN) groups according to nutritional support. ICU-AW was diagnosed using the Medical Research Council score. The primary outcome was the occurrence of ICU-AW.RESULTS: The significant factors associated with ICU-AW included age, sex, type of nutritional therapy, mechanical ventilation(MV), body mass index(BMI), blood urea nitrogen(BUN), and creatinine(Cr) levels(P<0.05). The PN group developed ICU-AW earlier than did the EEN group, with a significant difference observed(log-rank P<0.001). Among biomarkers for ICU-AW, the mean prealbumin(PAB)/C-reactive protein(CRP) ratio had the highest diagnostic accuracy(area under the curve [AUC] 0.928, 95% confidence interval [95% CI] 0.892–0.946), surpassing the mean Cr/BUN ratio(AUC 0.740, 95% CI 0.663–0.819) and mean transferrin levels(AUC 0.653, 95% CI 0.574–0.733).CONCLUSION: Independent risk factors for ICU-AW include female sex, advanced age, PN, MV, lower BMI, and elevated BUN and Cr levels. EEN may potentially delay ICU-AW onset, and the PAB/CRP ratio may be an effective diagnostic marker for this condition.
基金approved by our institutional Research Ethics Committee(HREC Approval Number H13554).
文摘Purpose:This study investigates key factors contributing to research impact and their interactions with the Research Impact Quintuple Helix Model by Arsalan et al.(2024).Design/methodology/approach:Using data from a global survey of 630 scientists across diverse disciplines,genders,regions,and experience levels,Structural Equation Modelling(SEM)was employed to assess the influence of 29 factors related to researcher characteristics,research attributes,publication strategies,institutional support,and national roles.Findings:The study validated the Quintuple Helix Model,uncovering complex interdependencies.Institutional support significantly affects research impact by covering leadership,resources,recognition,and funding.Researcher attributes,including academic experience and domain knowledge,also play a crucial role.National socioeconomic conditions indirectly influence research impact by supporting institutions,underscoring the importance of conducive national frameworks.Research limitations:While the study offers valuable insights,it has limitations.Although statistically sufficient,the response rate was below 10%,suggesting that the findings may not fully represent the entire global research community.The reliance on self-reported data may also introduce bias,as perceptions of impact can be subjective.Practical implications:The findings have a significant impact on researchers aiming to enhance their work’s societal,economic,and cultural significance,institutions seeking supportive environments,and policymakers interested in creating favourable national conditions for impactful research.The study advocates for a strategic alignment among national policies,institutional practices,and individual researcher efforts to maximise research impact and effectively address global challenges.Originality/value:By empirically validating the Research Impact Quintuple Helix Model,this study offers a holistic framework for understanding the synergy of factors that drive impactful research.
